All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

D2-Net: A Trainable CNN for Joint Description and Detectionof Local Features

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F19%3A00336316" target="_blank" >RIV/68407700:21730/19:00336316 - isvavai.cz</a>

  • Alternative codes found

    RIV/68407700:21730/19:00337394

  • Result on the web

    <a href="https://doi.org/10.1109/CVPR.2019.00828" target="_blank" >https://doi.org/10.1109/CVPR.2019.00828</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/CVPR.2019.00828" target="_blank" >10.1109/CVPR.2019.00828</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    D2-Net: A Trainable CNN for Joint Description and Detectionof Local Features

  • Original language description

    In this work, we address the problem of finding reliable pixel-level correspondences under difficult imaging conditions. We propose an approach where a single convolutional neural network plays a dual role: It is simultaneously a dense feature descriptor and a feature detector.By postponing the detection to a later stage, the obtained keypoints are more stable than their traditional counter-parts based on early detection of low-level structures. Weshow that this model can be trained using pixel correspondences extracted from readily available large-scale SfM reconstructions, without any further annotations. The proposed method obtains state-of-the-art performance on both the difficult Aachen Day-Night localization dataset and theInLoc indoor localization benchmark, as well as competitive performance on other benchmarks for image matching and 3D reconstruction.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2019

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    CVPR 2019: Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition

  • ISBN

    978-1-7281-3294-5

  • ISSN

    1063-6919

  • e-ISSN

    2575-7075

  • Number of pages

    10

  • Pages from-to

    8084-8093

  • Publisher name

    IEEE

  • Place of publication

  • Event location

    Long Beach

  • Event date

    Jun 15, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article